{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9e47c3c0",
   "metadata": {},
   "source": [
    "# 显示多个图\n",
    "使用 subplots() 函数，您可以在一张图中绘制多个图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e42feb6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b30a0774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot 1:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x,y)\n",
    "\n",
    "#plot 2:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([10, 20, 30, 40])\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x,y)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f3c9695",
   "metadata": {},
   "source": [
    "# subplots() 函数\n",
    "subplots() 函数采用三个参数来描述图形的布局。\n",
    "\n",
    "布局按行和列组织，由第一个和第二个参数表示。第三个参数表示当前绘图的索引。\n",
    "\n",
    "如果我们想要一个 2 行 1 列的图形（这意味着两个图将垂直显示而不是并排显示），我们可以编写如下语法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c50348f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot 1:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "# 该图有 2 行 1 列，该子图是第一个图。\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(x,y)\n",
    "\n",
    "#plot 2:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([10, 20, 30, 40])\n",
    "\n",
    "# 该图有 2 行 1 列，该子图是第二个图。\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x,y)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e4106908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.subplot(2, 3, 1)\n",
    "plt.plot(x,y)\n",
    "\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([10, 20, 30, 40])\n",
    "\n",
    "plt.subplot(2, 3, 2)\n",
    "plt.plot(x,y)\n",
    "\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.subplot(2, 3, 3)\n",
    "plt.plot(x,y)\n",
    "\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([10, 20, 30, 40])\n",
    "\n",
    "plt.subplot(2, 3, 4)\n",
    "plt.plot(x,y)\n",
    "\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.subplot(2, 3, 5)\n",
    "plt.plot(x,y)\n",
    "\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([10, 20, 30, 40])\n",
    "\n",
    "plt.subplot(2, 3, 6)\n",
    "plt.plot(x,y)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0957a10",
   "metadata": {},
   "source": [
    "# 标题\n",
    "使用 title() 函数为每个图添加标题："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "df31f991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot 1:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x,y)\n",
    "plt.title(\"SALES\")\n",
    "\n",
    "#plot 2:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([10, 20, 30, 40])\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x,y)\n",
    "plt.title(\"INCOME\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a24d5619",
   "metadata": {},
   "source": [
    "# 超级标题\n",
    "您可以使用 suptitle() 函数为整个图形添加标题："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a3704470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot 1:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x,y)\n",
    "plt.title(\"SALES\")\n",
    "\n",
    "#plot 2:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([10, 20, 30, 40])\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x,y)\n",
    "plt.title(\"INCOME\")\n",
    "\n",
    "plt.suptitle(\"MY SHOP\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
